KI_assignment1_KH

Kei Hayakawa created this text partially with GPT-4o and GPT-3.5, the large-scale language-generation model of OpenAI, and consulted their reply to make this. The author reviewed and modified the language, takes responsibility for this text.

Task1

2-a

Long noncoding RNAs (lncRNAs) are suggested to involved in phenotype change including cell growth, migration or proliferation of human dermal fibroblasts (HDFs). One of lncRNAs, ZNF213-AS1 was found to have some impact on cell migration and proliferation, it was more highly expressed in low-grade glioma (LGG) and acute myeloid leukemia (LAML) than other cancer types.

2-b

Antisense LNA-modified GapmeR antisense oligonucleotide (ASO) technology: It is kind of knock down technology which were used for knocking down 285 lncRNAs in this study to find out each lncRNA’s function.

Cap Analysis Gene Expression (CAGE): It is used to find out molecular phenotyping of lncRNAs knocked down HDFs.

3-a

1 Is there any relationships between ZNF213-AS1 expression and cancer heterogeneity? -> Yes, ZNF213-AS1 expression might depend on cancer development pathway variation.

2 How were functional lncRNAs obtained through evolution process? -> There might be lncRNA which should be the origin of lncRNAs’ regulating function.

3 Can we develop machine learning tools which can detect functional lncRNAs? -> Yes, using supervised learning and collecting functional lncRNAs’ sequences data might help developing that kind of tools.

Task3

# clear objects
rm(list = ls())

# install BiocManager 
# install.packages("BiocManager")

# install Tidyverse package
# install.packages("tidyverse")

# libraries
library(dplyr)
library(ggplot2)

Task4

# clear objects
rm(list = ls())

library(dplyr)

# describe data(CO2)
help("CO2")

# check the contents
CO2

Grouped Data: uptake ~ conc | Plant
   Plant        Type  Treatment conc uptake
1    Qn1      Quebec nonchilled   95   16.0
2    Qn1      Quebec nonchilled  175   30.4
3    Qn1      Quebec nonchilled  250   34.8
4    Qn1      Quebec nonchilled  350   37.2
5    Qn1      Quebec nonchilled  500   35.3
6    Qn1      Quebec nonchilled  675   39.2
7    Qn1      Quebec nonchilled 1000   39.7
8    Qn2      Quebec nonchilled   95   13.6
9    Qn2      Quebec nonchilled  175   27.3
10   Qn2      Quebec nonchilled  250   37.1
11   Qn2      Quebec nonchilled  350   41.8
12   Qn2      Quebec nonchilled  500   40.6
13   Qn2      Quebec nonchilled  675   41.4
14   Qn2      Quebec nonchilled 1000   44.3
15   Qn3      Quebec nonchilled   95   16.2
16   Qn3      Quebec nonchilled  175   32.4
17   Qn3      Quebec nonchilled  250   40.3
18   Qn3      Quebec nonchilled  350   42.1
19   Qn3      Quebec nonchilled  500   42.9
20   Qn3      Quebec nonchilled  675   43.9
21   Qn3      Quebec nonchilled 1000   45.5
22   Qc1      Quebec    chilled   95   14.2
23   Qc1      Quebec    chilled  175   24.1
24   Qc1      Quebec    chilled  250   30.3
25   Qc1      Quebec    chilled  350   34.6
26   Qc1      Quebec    chilled  500   32.5
27   Qc1      Quebec    chilled  675   35.4
28   Qc1      Quebec    chilled 1000   38.7
29   Qc2      Quebec    chilled   95    9.3
30   Qc2      Quebec    chilled  175   27.3
31   Qc2      Quebec    chilled  250   35.0
32   Qc2      Quebec    chilled  350   38.8
33   Qc2      Quebec    chilled  500   38.6
34   Qc2      Quebec    chilled  675   37.5
35   Qc2      Quebec    chilled 1000   42.4
36   Qc3      Quebec    chilled   95   15.1
37   Qc3      Quebec    chilled  175   21.0
38   Qc3      Quebec    chilled  250   38.1
39   Qc3      Quebec    chilled  350   34.0
40   Qc3      Quebec    chilled  500   38.9
41   Qc3      Quebec    chilled  675   39.6
42   Qc3      Quebec    chilled 1000   41.4
43   Mn1 Mississippi nonchilled   95   10.6
44   Mn1 Mississippi nonchilled  175   19.2
45   Mn1 Mississippi nonchilled  250   26.2
46   Mn1 Mississippi nonchilled  350   30.0
47   Mn1 Mississippi nonchilled  500   30.9
48   Mn1 Mississippi nonchilled  675   32.4
49   Mn1 Mississippi nonchilled 1000   35.5
50   Mn2 Mississippi nonchilled   95   12.0
51   Mn2 Mississippi nonchilled  175   22.0
52   Mn2 Mississippi nonchilled  250   30.6
53   Mn2 Mississippi nonchilled  350   31.8
54   Mn2 Mississippi nonchilled  500   32.4
55   Mn2 Mississippi nonchilled  675   31.1
56   Mn2 Mississippi nonchilled 1000   31.5
57   Mn3 Mississippi nonchilled   95   11.3
58   Mn3 Mississippi nonchilled  175   19.4
59   Mn3 Mississippi nonchilled  250   25.8
60   Mn3 Mississippi nonchilled  350   27.9
61   Mn3 Mississippi nonchilled  500   28.5
62   Mn3 Mississippi nonchilled  675   28.1
63   Mn3 Mississippi nonchilled 1000   27.8
64   Mc1 Mississippi    chilled   95   10.5
65   Mc1 Mississippi    chilled  175   14.9
66   Mc1 Mississippi    chilled  250   18.1
67   Mc1 Mississippi    chilled  350   18.9
68   Mc1 Mississippi    chilled  500   19.5
69   Mc1 Mississippi    chilled  675   22.2
70   Mc1 Mississippi    chilled 1000   21.9
71   Mc2 Mississippi    chilled   95    7.7
72   Mc2 Mississippi    chilled  175   11.4
73   Mc2 Mississippi    chilled  250   12.3
74   Mc2 Mississippi    chilled  350   13.0
75   Mc2 Mississippi    chilled  500   12.5
76   Mc2 Mississippi    chilled  675   13.7
77   Mc2 Mississippi    chilled 1000   14.4
78   Mc3 Mississippi    chilled   95   10.6
79   Mc3 Mississippi    chilled  175   18.0
80   Mc3 Mississippi    chilled  250   17.9
81   Mc3 Mississippi    chilled  350   17.9
82   Mc3 Mississippi    chilled  500   17.9
83   Mc3 Mississippi    chilled  675   18.9
84   Mc3 Mississippi    chilled 1000   19.9

# average and median of each state
CO2 |>
  group_by(Type) |>
  summarise(
    average = mean(uptake),
    median = median(uptake)
  )

Task5

5-1

# clear objects
rm(list = ls())

# make function which calculate mean and median of given vector
mean_and_median_ratio <- function(vect) {
  return(mean(vect) / median(vect))
}

# i.g.
vect1 <- c(10, 20, 30)
vect2 <- mean_and_median_ratio(vect1)
vect2

[1] 1

5-2

# make a function which ignore max and min of given vector
mean_without_max_min <- function(vect) {
  max <- max(vect)
  min <- min(vect)
  vect <- vect[vect != max & vect != min]
  return(mean(vect))
}

#i.g.
vect3 <- c(0, 10, 20, 30, 40, 50, 10000)
vect4 <- mean_without_max_min(vect3)
vect4

[1] 30

5-3

Piping is useful when we want to shorten the code which contains the multiple procedures, but we have to be careful to use it when the number of procedures is too large. Piping in that case makes scripts less readable for other developers, and also makes it difficult to find error in scripts. I think at least 3 times of piping are the maximum. We can use creating new variables and objects to avoid too long piping.

5-4

apply-family functions are used when we want to apply functions to rows of columns of a data frame or a list without using iterative process. Iterative process like for sentence makes scripts longer and complex to read. Using apply-family functions makes scripts much easier to read and easier to modify.

Task6

6-1

6-1-a

# import magic_guys.csv file to d1
d1 <- read.csv("/Users/kei.h/Desktop/KI_2024/ki_data/magic_guys.csv")

# check the contents
d1

# make histogram
d1_hist <- ggplot(
  data = d1,
  aes(x = length, fill = species)) +
  geom_histogram(bins = 10) +
  theme_bw()

# plot histogram
plot(d1_hist)

6-1-b

# make boxplot
d1_boxplot <- ggplot(data = d1, aes(x = species, y = length, fill = species)) +
  geom_boxplot() +
  theme_bw()

# plot boxplot
plot(d1_boxplot)

6-1-c

# save boxplot in png
# ggsave("magic_guys_boxplot.png")

# save as pdf
# ggsave("magic_guys_boxplot.pdf")

# save as svg
# ggsave("magic_guys_boxplot.svg")

6-2

6-2-a

# import tab file
d2 <- read.table("/Users/kei.h/Desktop/KI_2024/ki_data/microarray_data.tab",
                 header = TRUE,
                 sep = "\t",
                 na.strings = "")

# check the contents
d2

# data　size
d2_size <- dim(d2)
d2_size

[1]  553 1000

6-2-b

Calculate the number of missing values of each gene

# calculate the number of missing values 
missing_gene <- colSums(is.na(d2))
missing_gene

   g1    g2    g3    g4    g5    g6    g7    g8    g9   g10   g11   g12   g13   g14   g15   g16   g17   g18 
  130   104    74    93    81    30    31    26    12    56    72   105    55   133    75    67    21   553 
  g19   g20   g21   g22   g23   g24   g25   g26   g27   g28   g29   g30   g31   g32   g33   g34   g35   g36 
   13     9   207   190    96   141   122   149    47    59   210    22    14    52    14    24    70    83 
  g37   g38   g39   g40   g41   g42   g43   g44   g45   g46   g47   g48   g49   g50   g51   g52   g53   g54 
   64    91   144   124   216    64    13    70    61   108    68   404   106    91   230   231   207   195 
  g55   g56   g57   g58   g59   g60   g61   g62   g63   g64   g65   g66   g67   g68   g69   g70   g71   g72 
  553   196   186   553   224   362   198   217   208   210   188   351   187   189   187   194   185   185 
  g73   g74   g75   g76   g77   g78   g79   g80   g81   g82   g83   g84   g85   g86   g87   g88   g89   g90 
  354   203   216   216   245   201   553   189   188   224   553   213   188   194   192   213   219   229 
  g91   g92   g93   g94   g95   g96   g97   g98   g99  g100  g101  g102  g103  g104  g105  g106  g107  g108 
  365   223   369   353   195   230   250   238   553   204   228   199   192   211   373   251   261   207 
 g109  g110  g111  g112  g113  g114  g115  g116  g117  g118  g119  g120  g121  g122  g123  g124  g125  g126 
  362   187   200   209   210   224   203   205   189   212   236   260    33    26    25    22    14    48 
 g127  g128  g129  g130  g131  g132  g133  g134  g135  g136  g137  g138  g139  g140  g141  g142  g143  g144 
   36    42    18   194   213   367   198   199   553   185   553   553   191   186    14   177     8    13 
 g145  g146  g147  g148  g149  g150  g151  g152  g153  g154  g155  g156  g157  g158  g159  g160  g161  g162 
   17     8   186   202    21    30   198   186   207   185   188   196   215   186   188   187    36    27 
 g163  g164  g165  g166  g167  g168  g169  g170  g171  g172  g173  g174  g175  g176  g177  g178  g179  g180 
   28    18   212    36     8    23    33    23   226   376   200   215   193   214   185   224   203   193 
 g181  g182  g183  g184  g185  g186  g187  g188  g189  g190  g191  g192  g193  g194  g195  g196  g197  g198 
   10     9    10    18    32    47    31    44    15    20    36    35    36    61    20   188    12     8 
 g199  g200  g201  g202  g203  g204  g205  g206  g207  g208  g209  g210  g211  g212  g213  g214  g215  g216 
   12   187    12     7    19   215    15    12     8    11    23   173     9    14     7    29    15    36 
 g217  g218  g219  g220  g221  g222  g223  g224  g225  g226  g227  g228  g229  g230  g231  g232  g233  g234 
   15    10    16    10    56    31    83    52    27    27    12    13    38    44    17    24   188    11 
 g235  g236  g237  g238  g239  g240  g241  g242  g243  g244  g245  g246  g247  g248  g249  g250  g251  g252 
   36    30    39    55    64    38    81    66    65    61    24    26    47    26    36    23    47   203 
 g253  g254  g255  g256  g257  g258  g259  g260  g261  g262  g263  g264  g265  g266  g267  g268  g269  g270 
    2    20    17     8     7    70    34   392    31    49    67    60    38    36    18    61    38    32 
 g271  g272  g273  g274  g275  g276  g277  g278  g279  g280  g281  g282  g283  g284  g285  g286  g287  g288 
   12    14    15    61    24    22     8     3    26    89    67    18    27    92    59    65    65    57 
 g289  g290  g291  g292  g293  g294  g295  g296  g297  g298  g299  g300  g301  g302  g303  g304  g305  g306 
   53   410    17    97    15    60    68   106   127    64    81    39   388    34    10    25    14    18 
 g307  g308  g309  g310  g311  g312  g313  g314  g315  g316  g317  g318  g319  g320  g321  g322  g323  g324 
   40    55    57    47    92    61    82    89    70    83    11    15    30    98   115    74    57    90 
 g325  g326  g327  g328  g329  g330  g331  g332  g333  g334  g335  g336  g337  g338  g339  g340  g341  g342 
   54    53    72    51   553    19    91   147   113   116   130    60    84    47    70    34    18    23 
 g343  g344  g345  g346  g347  g348  g349  g350  g351  g352  g353  g354  g355  g356  g357  g358  g359  g360 
   25   150    48    54    56    58    46    33   238   553   220   200   388   216   218   211   212   205 
 g361  g362  g363  g364  g365  g366  g367  g368  g369  g370  g371  g372  g373  g374  g375  g376  g377  g378 
  192   370   371   208   200   191   240   553   186   248    50    65    45    71    20    29    74    93 
 g379  g380  g381  g382  g383  g384  g385  g386  g387  g388  g389  g390  g391  g392  g393  g394  g395  g396 
  239    59   212   205   409   204   207   225   241   358   553   553   402    57    53    55    45   211 
 g397  g398  g399  g400  g401  g402  g403  g404  g405  g406  g407  g408  g409  g410  g411  g412  g413  g414 
   22    47    39   136   208   200   191   210   248   367   195   196   196   210    57    29    84    40 
 g415  g416  g417  g418  g419  g420  g421  g422  g423  g424  g425  g426  g427  g428  g429  g430  g431  g432 
  117    62   553   210    91    41    75    13    69    33    59    55    24    32    94    79   553   359 
 g433  g434  g435  g436  g437  g438  g439  g440  g441  g442  g443  g444  g445  g446  g447  g448  g449  g450 
  243   185   190   215   206   230   205   391     6     8    47    17   132    12    30    17    37   195 
 g451  g452  g453  g454  g455  g456  g457  g458  g459  g460  g461  g462  g463  g464  g465  g466  g467  g468 
   17     9    86    30   202    10    20    33    63    61   392   356   195   190   220   229   188   189 
 g469  g470  g471  g472  g473  g474  g475  g476  g477  g478  g479  g480  g481  g482  g483  g484  g485  g486 
  205   245    10    28    37     6    21   190    70     9   110    23    87    29    80    30    14    23 
 g487  g488  g489  g490  g491  g492  g493  g494  g495  g496  g497  g498  g499  g500  g501  g502  g503  g504 
   14    11    23    33   215   187   201   190   188   221   224   553   190   192    39    70    10     9 
 g505  g506  g507  g508  g509  g510  g511  g512  g513  g514  g515  g516  g517  g518  g519  g520  g521  g522 
   36    29    64     9    13   189    17    16   184    72   214    43    39   217   553   190    48   200 
 g523  g524  g525  g526  g527  g528  g529  g530  g531  g532  g533  g534  g535  g536  g537  g538  g539  g540 
    4    63    61    27   553    36    22    73   553   387   197   192   209   207   238   553   185   201 
 g541  g542  g543  g544  g545  g546  g547  g548  g549  g550  g551  g552  g553  g554  g555  g556  g557  g558 
   56    41    14   189     8    12   224    25    13    46    18    26    77    23    80    65    28   203 
 g559  g560  g561  g562  g563  g564  g565  g566  g567  g568  g569  g570  g571  g572  g573  g574  g575  g576 
   96    44   227    11    56    66    40    54    94    55   204   198   203   417   249   192   377   553 
 g577  g578  g579  g580  g581  g582  g583  g584  g585  g586  g587  g588  g589  g590  g591  g592  g593  g594 
  553   190   189   197    28    19   188     8   386   197    15    29    93    11   181    57    22    28 
 g595  g596  g597  g598  g599  g600  g601  g602  g603  g604  g605  g606  g607  g608  g609  g610  g611  g612 
  105    30    59    14   188     3    23    27     2    46    21    19    63    50    18    85   187   219 
 g613  g614  g615  g616  g617  g618  g619  g620  g621  g622  g623  g624  g625  g626  g627  g628  g629  g630 
  212   210   553   190   209   234   356   198     8    20    32    15    16    26    24    10    12    21 
 g631  g632  g633  g634  g635  g636  g637  g638  g639  g640  g641  g642  g643  g644  g645  g646  g647  g648 
   67    27    11    12    17    14    25    76    12    43    14    11    49    14    16    10    17    26 
 g649  g650  g651  g652  g653  g654  g655  g656  g657  g658  g659  g660  g661  g662  g663  g664  g665  g666 
   10   211    13    16    75    52    11     7   216     9    26    70    13    12   398     8    16    71 
 g667  g668  g669  g670  g671  g672  g673  g674  g675  g676  g677  g678  g679  g680  g681  g682  g683  g684 
   11    14   372     8    14    77    22    13    40    12    25    50    36    11    59    14    21    17 
 g685  g686  g687  g688  g689  g690  g691  g692  g693  g694  g695  g696  g697  g698  g699  g700  g701  g702 
   11    32    19    36   224     3   199     6     5   183    10    63     7    32     7    56    43    39 
 g703  g704  g705  g706  g707  g708  g709  g710  g711  g712  g713  g714  g715  g716  g717  g718  g719  g720 
   32    26    54    20   104    15    56    35   108    34    22    32    69    25    29    69    60    23 
 g721  g722  g723  g724  g725  g726  g727  g728  g729  g730  g731  g732  g733  g734  g735  g736  g737  g738 
   26    68    24   101    10    58    14    13    18    18    41    32    10    25    37    19    23    17 
 g739  g740  g741  g742  g743  g744  g745  g746  g747  g748  g749  g750  g751  g752  g753  g754  g755  g756 
   12    31    19    11    59   190    33    21    69    66    58    14   553   187   553   204   187   210 
 g757  g758  g759  g760  g761  g762  g763  g764  g765  g766  g767  g768  g769  g770  g771  g772  g773  g774 
  193   212   208   231   191   188   235   188   215   355   195   356   199   187    29    38    41    23 
 g775  g776  g777  g778  g779  g780  g781  g782  g783  g784  g785  g786  g787  g788  g789  g790  g791  g792 
   12    72    21    40    34    48   191   222   219   227   209   221   223   553   249   195    13    25 
 g793  g794  g795  g796  g797  g798  g799  g800  g801  g802  g803  g804  g805  g806  g807  g808  g809  g810 
   15    22    63    90     4    17    51    33   242   553   189   385   189   205   211   209   235   219 
 g811  g812  g813  g814  g815  g816  g817  g818  g819  g820  g821  g822  g823  g824  g825  g826  g827  g828 
   29    89    22   146    20    55    16    80    28    74    32   107    18    91    28    22    22    18 
 g829  g830  g831  g832  g833  g834  g835  g836  g837  g838  g839  g840  g841  g842  g843  g844  g845  g846 
   38    23   192   229   227   186   186   207   191   553   196   200    17     9    57    33    16    13 
 g847  g848  g849  g850  g851  g852  g853  g854  g855  g856  g857  g858  g859  g860  g861  g862  g863  g864 
   21    20   189   201   388    34     9   386    35    10    11    14    17    26   186   223   236   553 
 g865  g866  g867  g868  g869  g870  g871  g872  g873  g874  g875  g876  g877  g878  g879  g880  g881  g882 
  228   188   216   191   217   211    36   195    27    77    23    32    23    38    13    38    41    57 
 g883  g884  g885  g886  g887  g888  g889  g890  g891  g892  g893  g894  g895  g896  g897  g898  g899  g900 
   15    71    25    37    53    18    20    38   189   553   553   202   194   225   252   393   244   225 
 g901  g902  g903  g904  g905  g906  g907  g908  g909  g910  g911  g912  g913  g914  g915  g916  g917  g918 
   11    19    85    35    15    35    35    34   108   191    79    30    20    19    52    15    30    35 
 g919  g920  g921  g922  g923  g924  g925  g926  g927  g928  g929  g930  g931  g932  g933  g934  g935  g936 
  553    34    48    71    28    55    44   192    19    10    30    26   185   376   224   232   192   250 
 g937  g938  g939  g940  g941  g942  g943  g944  g945  g946  g947  g948  g949  g950  g951  g952  g953  g954 
  217   202   228   199    18    17    22    10    70    32   199    69    36    13   187    57    31    12 
 g955  g956  g957  g958  g959  g960  g961  g962  g963  g964  g965  g966  g967  g968  g969  g970  g971  g972 
   21    68    26    26    16    19    14    53    56    20    83    10    11    14    26   191   553   225 
 g973  g974  g975  g976  g977  g978  g979  g980  g981  g982  g983  g984  g985  g986  g987  g988  g989  g990 
  228   222   211   227   184   195   205   354    11    50    10    32   188    13    13    14   201    21 
 g991  g992  g993  g994  g995  g996  g997  g998  g999 g1000 
   13    13    20    29    12    33    18    19    24    17 

Make a data frame of gene names, missing values and missing value ratio

# make a data frame
missing_d <- data.frame(gene_name = names(missing_gene),
                        missing_values = as.numeric(missing_gene),
                        missing_value_ratio = as.numeric(missing_gene) / nrow(d2))

# check the contents
missing_d

Visualizing

# make a histogram of "missing value ratio"
missingg_hist <- ggplot(data = missing_d, aes(x = missing_value_ratio)) +
  geom_histogram(bins = 10) +
  theme_bw()

plot(missingg_hist)

6-2-c

# genes over 10%, 20%, 50% criteria
c1 <- c(0.1, 0.2, 0.5)
high_gene <- lapply(c1, 
                    function(criteria){
                      genes <- missing_d$gene_name[missing_d$missing_value_ratio > criteria]
                      return(genes)
                    })

# check the contents, >10%, >20%, >50%
high_gene

[[1]]
  [1] "g1"   "g2"   "g3"   "g4"   "g5"   "g10"  "g11"  "g12"  "g14"  "g15"  "g16"  "g18"  "g21"  "g22"  "g23" 
 [16] "g24"  "g25"  "g26"  "g28"  "g29"  "g35"  "g36"  "g37"  "g38"  "g39"  "g40"  "g41"  "g42"  "g44"  "g45" 
 [31] "g46"  "g47"  "g48"  "g49"  "g50"  "g51"  "g52"  "g53"  "g54"  "g55"  "g56"  "g57"  "g58"  "g59"  "g60" 
 [46] "g61"  "g62"  "g63"  "g64"  "g65"  "g66"  "g67"  "g68"  "g69"  "g70"  "g71"  "g72"  "g73"  "g74"  "g75" 
 [61] "g76"  "g77"  "g78"  "g79"  "g80"  "g81"  "g82"  "g83"  "g84"  "g85"  "g86"  "g87"  "g88"  "g89"  "g90" 
 [76] "g91"  "g92"  "g93"  "g94"  "g95"  "g96"  "g97"  "g98"  "g99"  "g100" "g101" "g102" "g103" "g104" "g105"
 [91] "g106" "g107" "g108" "g109" "g110" "g111" "g112" "g113" "g114" "g115" "g116" "g117" "g118" "g119" "g120"
[106] "g130" "g131" "g132" "g133" "g134" "g135" "g136" "g137" "g138" "g139" "g140" "g142" "g147" "g148" "g151"
[121] "g152" "g153" "g154" "g155" "g156" "g157" "g158" "g159" "g160" "g165" "g171" "g172" "g173" "g174" "g175"
[136] "g176" "g177" "g178" "g179" "g180" "g194" "g196" "g200" "g204" "g210" "g221" "g223" "g233" "g239" "g241"
[151] "g242" "g243" "g244" "g252" "g258" "g260" "g263" "g264" "g268" "g274" "g280" "g281" "g284" "g285" "g286"
[166] "g287" "g288" "g290" "g292" "g294" "g295" "g296" "g297" "g298" "g299" "g301" "g309" "g311" "g312" "g313"
[181] "g314" "g315" "g316" "g320" "g321" "g322" "g323" "g324" "g327" "g329" "g331" "g332" "g333" "g334" "g335"
[196] "g336" "g337" "g339" "g344" "g347" "g348" "g351" "g352" "g353" "g354" "g355" "g356" "g357" "g358" "g359"
[211] "g360" "g361" "g362" "g363" "g364" "g365" "g366" "g367" "g368" "g369" "g370" "g372" "g374" "g377" "g378"
[226] "g379" "g380" "g381" "g382" "g383" "g384" "g385" "g386" "g387" "g388" "g389" "g390" "g391" "g392" "g396"
[241] "g400" "g401" "g402" "g403" "g404" "g405" "g406" "g407" "g408" "g409" "g410" "g411" "g413" "g415" "g416"
[256] "g417" "g418" "g419" "g421" "g423" "g425" "g429" "g430" "g431" "g432" "g433" "g434" "g435" "g436" "g437"
[271] "g438" "g439" "g440" "g445" "g450" "g453" "g455" "g459" "g460" "g461" "g462" "g463" "g464" "g465" "g466"
[286] "g467" "g468" "g469" "g470" "g476" "g477" "g479" "g481" "g483" "g491" "g492" "g493" "g494" "g495" "g496"
[301] "g497" "g498" "g499" "g500" "g502" "g507" "g510" "g513" "g514" "g515" "g518" "g519" "g520" "g522" "g524"
[316] "g525" "g527" "g530" "g531" "g532" "g533" "g534" "g535" "g536" "g537" "g538" "g539" "g540" "g541" "g544"
[331] "g547" "g553" "g555" "g556" "g558" "g559" "g561" "g563" "g564" "g567" "g569" "g570" "g571" "g572" "g573"
[346] "g574" "g575" "g576" "g577" "g578" "g579" "g580" "g583" "g585" "g586" "g589" "g591" "g592" "g595" "g597"
[361] "g599" "g607" "g610" "g611" "g612" "g613" "g614" "g615" "g616" "g617" "g618" "g619" "g620" "g631" "g638"
[376] "g650" "g653" "g657" "g660" "g663" "g666" "g669" "g672" "g681" "g689" "g691" "g694" "g696" "g700" "g707"
[391] "g709" "g711" "g715" "g718" "g719" "g722" "g724" "g726" "g743" "g744" "g747" "g748" "g749" "g751" "g752"
[406] "g753" "g754" "g755" "g756" "g757" "g758" "g759" "g760" "g761" "g762" "g763" "g764" "g765" "g766" "g767"
[421] "g768" "g769" "g770" "g776" "g781" "g782" "g783" "g784" "g785" "g786" "g787" "g788" "g789" "g790" "g795"
[436] "g796" "g801" "g802" "g803" "g804" "g805" "g806" "g807" "g808" "g809" "g810" "g812" "g814" "g818" "g820"
[451] "g822" "g824" "g831" "g832" "g833" "g834" "g835" "g836" "g837" "g838" "g839" "g840" "g843" "g849" "g850"
[466] "g851" "g854" "g861" "g862" "g863" "g864" "g865" "g866" "g867" "g868" "g869" "g870" "g872" "g874" "g882"
[481] "g884" "g891" "g892" "g893" "g894" "g895" "g896" "g897" "g898" "g899" "g900" "g903" "g909" "g910" "g911"
[496] "g919" "g922" "g926" "g931" "g932" "g933" "g934" "g935" "g936" "g937" "g938" "g939" "g940" "g945" "g947"
[511] "g948" "g951" "g952" "g956" "g963" "g965" "g970" "g971" "g972" "g973" "g974" "g975" "g976" "g977" "g978"
[526] "g979" "g980" "g985" "g989"

[[2]]
  [1] "g1"   "g14"  "g18"  "g21"  "g22"  "g24"  "g25"  "g26"  "g29"  "g39"  "g40"  "g41"  "g48"  "g51"  "g52" 
 [16] "g53"  "g54"  "g55"  "g56"  "g57"  "g58"  "g59"  "g60"  "g61"  "g62"  "g63"  "g64"  "g65"  "g66"  "g67" 
 [31] "g68"  "g69"  "g70"  "g71"  "g72"  "g73"  "g74"  "g75"  "g76"  "g77"  "g78"  "g79"  "g80"  "g81"  "g82" 
 [46] "g83"  "g84"  "g85"  "g86"  "g87"  "g88"  "g89"  "g90"  "g91"  "g92"  "g93"  "g94"  "g95"  "g96"  "g97" 
 [61] "g98"  "g99"  "g100" "g101" "g102" "g103" "g104" "g105" "g106" "g107" "g108" "g109" "g110" "g111" "g112"
 [76] "g113" "g114" "g115" "g116" "g117" "g118" "g119" "g120" "g130" "g131" "g132" "g133" "g134" "g135" "g136"
 [91] "g137" "g138" "g139" "g140" "g142" "g147" "g148" "g151" "g152" "g153" "g154" "g155" "g156" "g157" "g158"
[106] "g159" "g160" "g165" "g171" "g172" "g173" "g174" "g175" "g176" "g177" "g178" "g179" "g180" "g196" "g200"
[121] "g204" "g210" "g233" "g252" "g260" "g290" "g297" "g301" "g321" "g329" "g332" "g333" "g334" "g335" "g344"
[136] "g351" "g352" "g353" "g354" "g355" "g356" "g357" "g358" "g359" "g360" "g361" "g362" "g363" "g364" "g365"
[151] "g366" "g367" "g368" "g369" "g370" "g379" "g381" "g382" "g383" "g384" "g385" "g386" "g387" "g388" "g389"
[166] "g390" "g391" "g396" "g400" "g401" "g402" "g403" "g404" "g405" "g406" "g407" "g408" "g409" "g410" "g415"
[181] "g417" "g418" "g431" "g432" "g433" "g434" "g435" "g436" "g437" "g438" "g439" "g440" "g445" "g450" "g455"
[196] "g461" "g462" "g463" "g464" "g465" "g466" "g467" "g468" "g469" "g470" "g476" "g491" "g492" "g493" "g494"
[211] "g495" "g496" "g497" "g498" "g499" "g500" "g510" "g513" "g515" "g518" "g519" "g520" "g522" "g527" "g531"
[226] "g532" "g533" "g534" "g535" "g536" "g537" "g538" "g539" "g540" "g544" "g547" "g558" "g561" "g569" "g570"
[241] "g571" "g572" "g573" "g574" "g575" "g576" "g577" "g578" "g579" "g580" "g583" "g585" "g586" "g591" "g599"
[256] "g611" "g612" "g613" "g614" "g615" "g616" "g617" "g618" "g619" "g620" "g650" "g657" "g663" "g669" "g689"
[271] "g691" "g694" "g744" "g751" "g752" "g753" "g754" "g755" "g756" "g757" "g758" "g759" "g760" "g761" "g762"
[286] "g763" "g764" "g765" "g766" "g767" "g768" "g769" "g770" "g781" "g782" "g783" "g784" "g785" "g786" "g787"
[301] "g788" "g789" "g790" "g801" "g802" "g803" "g804" "g805" "g806" "g807" "g808" "g809" "g810" "g814" "g831"
[316] "g832" "g833" "g834" "g835" "g836" "g837" "g838" "g839" "g840" "g849" "g850" "g851" "g854" "g861" "g862"
[331] "g863" "g864" "g865" "g866" "g867" "g868" "g869" "g870" "g872" "g891" "g892" "g893" "g894" "g895" "g896"
[346] "g897" "g898" "g899" "g900" "g910" "g919" "g926" "g931" "g932" "g933" "g934" "g935" "g936" "g937" "g938"
[361] "g939" "g940" "g947" "g951" "g970" "g971" "g972" "g973" "g974" "g975" "g976" "g977" "g978" "g979" "g980"
[376] "g985" "g989"

[[3]]
 [1] "g18"  "g48"  "g55"  "g58"  "g60"  "g66"  "g73"  "g79"  "g83"  "g91"  "g93"  "g94"  "g99"  "g105" "g109"
[16] "g132" "g135" "g137" "g138" "g172" "g260" "g290" "g301" "g329" "g352" "g355" "g362" "g363" "g368" "g383"
[31] "g388" "g389" "g390" "g391" "g406" "g417" "g431" "g432" "g440" "g461" "g462" "g498" "g519" "g527" "g531"
[46] "g532" "g538" "g572" "g575" "g576" "g577" "g585" "g615" "g619" "g663" "g669" "g751" "g753" "g766" "g768"
[61] "g788" "g802" "g804" "g838" "g851" "g854" "g864" "g892" "g893" "g898" "g919" "g932" "g971" "g980"

6-2-d

# calculate mean of each gene expression
mean_expression_of_each_gene <- sapply(d2, 
                                  function(expression_values){
                                    mean <- mean(expression_values, na.rm=TRUE)
                                    return(mean)
                                  })
# make data frame
mean_expression_of_each_gene_d <- data.frame(gene_name = names(mean_expression_of_each_gene),
                                        mean_expression = as.numeric(mean_expression_of_each_gene))

# check the contents
mean_expression_of_each_gene_d

# replace missing values with mean expression value of each gene
# make new data frame d3
d3 <- d2 %>%
  mutate(across(everything(), ~ifelse(
    is.na(.),
    mean_expression_of_each_gene_d$mean_expression[
      mean_expression_of_each_gene_d$gene_name==cur_column()],
    .
  )))

# check the contents
d3

6-3

# check the contents again
CO2

Grouped Data: uptake ~ conc | Plant
   Plant        Type  Treatment conc uptake
1    Qn1      Quebec nonchilled   95   16.0
2    Qn1      Quebec nonchilled  175   30.4
3    Qn1      Quebec nonchilled  250   34.8
4    Qn1      Quebec nonchilled  350   37.2
5    Qn1      Quebec nonchilled  500   35.3
6    Qn1      Quebec nonchilled  675   39.2
7    Qn1      Quebec nonchilled 1000   39.7
8    Qn2      Quebec nonchilled   95   13.6
9    Qn2      Quebec nonchilled  175   27.3
10   Qn2      Quebec nonchilled  250   37.1
11   Qn2      Quebec nonchilled  350   41.8
12   Qn2      Quebec nonchilled  500   40.6
13   Qn2      Quebec nonchilled  675   41.4
14   Qn2      Quebec nonchilled 1000   44.3
15   Qn3      Quebec nonchilled   95   16.2
16   Qn3      Quebec nonchilled  175   32.4
17   Qn3      Quebec nonchilled  250   40.3
18   Qn3      Quebec nonchilled  350   42.1
19   Qn3      Quebec nonchilled  500   42.9
20   Qn3      Quebec nonchilled  675   43.9
21   Qn3      Quebec nonchilled 1000   45.5
22   Qc1      Quebec    chilled   95   14.2
23   Qc1      Quebec    chilled  175   24.1
24   Qc1      Quebec    chilled  250   30.3
25   Qc1      Quebec    chilled  350   34.6
26   Qc1      Quebec    chilled  500   32.5
27   Qc1      Quebec    chilled  675   35.4
28   Qc1      Quebec    chilled 1000   38.7
29   Qc2      Quebec    chilled   95    9.3
30   Qc2      Quebec    chilled  175   27.3
31   Qc2      Quebec    chilled  250   35.0
32   Qc2      Quebec    chilled  350   38.8
33   Qc2      Quebec    chilled  500   38.6
34   Qc2      Quebec    chilled  675   37.5
35   Qc2      Quebec    chilled 1000   42.4
36   Qc3      Quebec    chilled   95   15.1
37   Qc3      Quebec    chilled  175   21.0
38   Qc3      Quebec    chilled  250   38.1
39   Qc3      Quebec    chilled  350   34.0
40   Qc3      Quebec    chilled  500   38.9
41   Qc3      Quebec    chilled  675   39.6
42   Qc3      Quebec    chilled 1000   41.4
43   Mn1 Mississippi nonchilled   95   10.6
44   Mn1 Mississippi nonchilled  175   19.2
45   Mn1 Mississippi nonchilled  250   26.2
46   Mn1 Mississippi nonchilled  350   30.0
47   Mn1 Mississippi nonchilled  500   30.9
48   Mn1 Mississippi nonchilled  675   32.4
49   Mn1 Mississippi nonchilled 1000   35.5
50   Mn2 Mississippi nonchilled   95   12.0
51   Mn2 Mississippi nonchilled  175   22.0
52   Mn2 Mississippi nonchilled  250   30.6
53   Mn2 Mississippi nonchilled  350   31.8
54   Mn2 Mississippi nonchilled  500   32.4
55   Mn2 Mississippi nonchilled  675   31.1
56   Mn2 Mississippi nonchilled 1000   31.5
57   Mn3 Mississippi nonchilled   95   11.3
58   Mn3 Mississippi nonchilled  175   19.4
59   Mn3 Mississippi nonchilled  250   25.8
60   Mn3 Mississippi nonchilled  350   27.9
61   Mn3 Mississippi nonchilled  500   28.5
62   Mn3 Mississippi nonchilled  675   28.1
63   Mn3 Mississippi nonchilled 1000   27.8
64   Mc1 Mississippi    chilled   95   10.5
65   Mc1 Mississippi    chilled  175   14.9
66   Mc1 Mississippi    chilled  250   18.1
67   Mc1 Mississippi    chilled  350   18.9
68   Mc1 Mississippi    chilled  500   19.5
69   Mc1 Mississippi    chilled  675   22.2
70   Mc1 Mississippi    chilled 1000   21.9
71   Mc2 Mississippi    chilled   95    7.7
72   Mc2 Mississippi    chilled  175   11.4
73   Mc2 Mississippi    chilled  250   12.3
74   Mc2 Mississippi    chilled  350   13.0
75   Mc2 Mississippi    chilled  500   12.5
76   Mc2 Mississippi    chilled  675   13.7
77   Mc2 Mississippi    chilled 1000   14.4
78   Mc3 Mississippi    chilled   95   10.6
79   Mc3 Mississippi    chilled  175   18.0
80   Mc3 Mississippi    chilled  250   17.9
81   Mc3 Mississippi    chilled  350   17.9
82   Mc3 Mississippi    chilled  500   17.9
83   Mc3 Mississippi    chilled  675   18.9
84   Mc3 Mississippi    chilled 1000   19.9

# make scatter plot and lm line
lm_CO2 <- ggplot(CO2, aes(x=conc, y=uptake, color=Type)) +
  geom_point() +
  geom_smooth(method = "lm") +
  scale_color_manual(values = c("Quebec"="red", "Mississippi"="blue")) +
  theme_bw()

plot(lm_CO2)

`geom_smooth()` using formula = 'y ~ x'

From this plot, it is suggested that “Quebec” has higher ability of CO2 absorption.

Task7

# install.packages("devtools")

# install tidybiology
# devtools::install_github("hirscheylab/tidybiology")

# import tidybiology chromosome
library(tidybiology)
chromosome <- tidybiology::chromosome

chromosome

# import tidybiology proteins
protein <- tidybiology::proteins

protein

7-1

7-1-a

# make a summary of chromosome data
summary_chr <- summary(chromosome)
summary_chr

       id       length_mm       basepairs           variations       protein_codinggenes  pseudo_genes   
 1      : 1   Min.   :16.00   Min.   : 46709983   Min.   :  211643   Min.   :  71.0      Min.   : 185.0  
 2      : 1   1st Qu.:27.75   1st Qu.: 82536402   1st Qu.: 4395298   1st Qu.: 595.8      1st Qu.: 445.8  
 3      : 1   Median :45.50   Median :133536366   Median : 6172346   Median : 836.0      Median : 590.5  
 4      : 1   Mean   :43.83   Mean   :128677910   Mean   : 6484572   Mean   : 850.0      Mean   : 608.3  
 5      : 1   3rd Qu.:55.00   3rd Qu.:162210974   3rd Qu.: 8742592   3rd Qu.:1055.5      3rd Qu.: 772.5  
 6      : 1   Max.   :85.00   Max.   :248956422   Max.   :12945965   Max.   :2058.0      Max.   :1220.0  
 (Other):18                                                                                              
 totallongnc_rna  totalsmallnc_rna     mi_rna           r_rna           sn_rna          sno_rna      
 Min.   :  71.0   Min.   : 30.0    Min.   : 15.00   Min.   : 5.00   Min.   : 17.00   Min.   :  3.00  
 1st Qu.: 439.0   1st Qu.:167.0    1st Qu.: 55.75   1st Qu.:13.00   1st Qu.: 49.75   1st Qu.: 36.75  
 Median : 633.5   Median :220.5    Median : 75.00   Median :23.00   Median : 77.00   Median : 59.50  
 Mean   : 613.6   Mean   :208.9    Mean   : 73.17   Mean   :22.08   Mean   : 81.00   Mean   : 63.38  
 3rd Qu.: 751.0   3rd Qu.:236.0    3rd Qu.: 92.00   3rd Qu.:27.25   3rd Qu.:106.00   3rd Qu.: 76.00  
 Max.   :1200.0   Max.   :496.0    Max.   :134.00   Max.   :66.00   Max.   :221.00   Max.   :145.00  
                                                                                                     
   miscnc_rna     centromereposition_mbp
 Min.   :  8.00   Min.   : 12.50        
 1st Qu.: 66.50   1st Qu.: 18.73        
 Median : 94.50   Median : 38.40        
 Mean   : 92.21   Mean   : 43.36        
 3rd Qu.:107.50   3rd Qu.: 55.25        
 Max.   :192.00   Max.   :125.00        
                                        

7-1-b

# make histogram of chromosome length (mm)
chr_size_hist <- ggplot(data = chromosome, aes(x = length_mm)) +
  geom_histogram(bins = 10) +
  theme_bw()

plot(chr_size_hist)

7-1-c

make scatter plot and lm line of number of protein coding genes and chromosome length

chr_size_prot_gene <- ggplot(data = chromosome, aes(x = length_mm, y = protein_codinggenes)) +
  geom_point() +
  geom_smooth(method = "lm") +
  theme_bw()

plot(chr_size_prot_gene)

`geom_smooth()` using formula = 'y ~ x'

There might be positive correlation between chromosome length and the number of protein coding genes

make scatter plot and lm line of number of miRNAs and chromosome length (mm)

chr_size_miRNA <- ggplot(data = chromosome, aes(x = length_mm, y = mi_rna)) +
  geom_point() +
  geom_smooth(method = "lm") +
  theme_bw()

plot(chr_size_miRNA)

`geom_smooth()` using formula = 'y ~ x'

There might be positive correlation between chromosome length and the number of miRNAs

7-1-d

make summary of protein data

summary_prot <- summary(protein)
summary_prot

  uniprot_id         gene_name         gene_name_alt      protein_name       protein_name_alt  
 Length:20430       Length:20430       Length:20430       Length:20430       Length:20430      
 Class :character   Class :character   Class :character   Class :character   Class :character  
 Mode  :character   Mode  :character   Mode  :character   Mode  :character   Mode  :character  
                                                                                               
                                                                                               
                                                                                               
   sequence             length             mass        
 Length:20430       Min.   :    2.0   Min.   :    260  
 Class :character   1st Qu.:  251.0   1st Qu.:  27940  
 Mode  :character   Median :  414.0   Median :  46140  
                    Mean   :  557.2   Mean   :  62061  
                    3rd Qu.:  669.0   3rd Qu.:  74755  
                    Max.   :34350.0   Max.   :3816030  

make a scatter plot and lm line with protein mass and length

prot_size_mass <- ggplot(data = protein, aes(x = length, y = mass)) +
  geom_point() +
  geom_smooth(method = "lm") +
  theme_bw()

plot(prot_size_mass)

`geom_smooth()` using formula = 'y ~ x'